name: verified-translator description: "Verified translation, internet slang decoding, persona voice transfer, and AI-to-AI style conversion. Supports cross-language, same-language, human-to-AI, and AI-to-human expression transfer with real-person style profiles." argument-hint: "[text-to-translate or mode]" version: "2.0.0" user-invocable: true allowed-tools: Read, Write, Edit, Bash, WebSearch, WebFetch

Language: Detect the user's language from their first message and respond in the same language throughout.

Verified Translator & Expression Transfer Skill

Translate the meaning first, then transfer the voice. Not just words — tone, subtext, and persona.

Trigger Conditions

Activate when the user says any of the following:

/translate
"translate", "what does this mean", "explain this slang"
"say it like X would", "rewrite in X tone", "decode", "localize"
"in GPT style", "in Musk's voice", "as Lu Xun would write"
"用鲁迅的口吻说", "翻译成互联网黑话", "用 GPT 风格改写"
"翻译", "黑话", "潜台词", "改写", "润色"
Pasted foreign text, internet slang, or any cross-language / cross-register / cross-persona request

Tool Usage

| Task | Tool | |------|------| | Terminology / background verification | LLM's built-in web search (WebSearch / WebFetch) — search first, translate second when uncertain | | Glossary cache lookup/write | Bash → python3 ${CLAUDE_SKILL_DIR}/tools/glossary_manager.py | | Translation history log/search | Bash → python3 ${CLAUDE_SKILL_DIR}/tools/history_manager.py | | Read user-uploaded files | Read tool |

Core rule: when in doubt, search. Any uncertain term, meme, person's public speaking style, or current reference — use web search first, then translate.

Main Flow

Step 1: Detect Intent

Infer from user input:

Mode: which of the 5 modes (or combination); default to Mode 1 if unclear
Source / target language: auto-detect; "same" for same-language style transfer
Style / persona: did the user specify a person, platform, tone, or scene?

Step 2: Web Verification (as needed)

Search the web before translating when encountering:

Specialized terminology, industry jargon, policy names
Official translations of org names, product names, person names
New internet memes, current-event references
A specific person's public speaking style (search their speeches, articles, tweets)
Anything you're not confident about

Step 3: Execute Translation

Reference the corresponding prompt template (${CLAUDE_SKILL_DIR}/prompts/):

| Mode | Prompt Template | |------|----------------| | Mode 1: Verified Translation | prompts/verified_translation.md | | Mode 2: Native Localization | prompts/native_localization.md | | Mode 3: Subtext & Slang Decode | prompts/subtext_decode.md | | Mode 4: Voice Transfer | prompts/voice_transfer.md | | Mode 5: AI-to-AI Translation | prompts/ai_translation.md | | Cross-mode: Terminology Grounding | prompts/terminology_grounding.md | | Cross-mode: Quality Check | prompts/quality_check.md |

Step 4: Output

Simple requests: just give the most useful result. Complex requests: use layered output structure.

Step 5: Persist (optional)

python3 ${CLAUDE_SKILL_DIR}/tools/glossary_manager.py \
  --action add --term "TERM" --translation "TRANSLATION" --domain "DOMAIN"
python3 ${CLAUDE_SKILL_DIR}/tools/history_manager.py \
  --action log --source-lang zh --target-lang en --mode "mode4" \
  --source-text "source" --result-summary "result"

Five Modes

Mode 1: Verified Translation

Cross-language translation with reliability guarantees.

Process:

Identify source/target languages
For domain content (legal, medical, technical, academic): web search first for official bilingual resources and standard terminology
Terminology grounding — lock key terms before translating full text; same term must be consistent throughout
Entity & number check: proper nouns, org names, dates, amounts, units verified individually
Ambiguity flagging: when a word/phrase has multiple valid readings, present candidates instead of guessing
Confidence signal: High / Medium / Low

When to web search:

Any uncertain specialized terminology
Official translations of org names, policy terms, product names
Recent or region-specific references
Any entity where mistranslation would cause real harm

Mode 2: Native Localization

Not word-for-word — "how a native speaker would actually say this."

Restructure syntax to target-language norms
Match formality register to context
Preserve pragmatic force (a polite refusal stays a polite refusal)
For social media content, match platform conventions of the target culture

Mode 3: Subtext & Slang Decode

For content where literal meaning ≠ real meaning.

Covered scenes:

Workplace subtext (Chinese examples):

"这个需求很简单" → probably not simple; speaker doesn't want you to think it's hard
"我们对齐一下" → what you did doesn't match what I expected
"你看着办" → I don't want to own this decision; if it goes wrong it's on you
"有空聊聊" → we need to talk, probably not good news
"方案挺有意思" → likely hedging (~50%), possibly skeptical (~30%), rarely genuine (~20%)

Dating / social subtext (Chinese examples):

"你是个好人" → classic "nice person card" — rejection
"我考虑一下" → most likely no, searching for words
"随便" → not "whatever" at all — guess correctly
"没事" → something is definitely wrong, figure it out yourself
"哦" → angry / bored / dismissive

Internet slang (Chinese examples):

"666" → impressive (sometimes ironic)
"yyds" (永远的神) → "GOAT" / greatest of all time
"破防了" → emotional defenses broken; deeply moved
"蚌埠住了" → can't hold it together (laughing)
"DNA动了" → triggered a deep memory/instinct
"典中典" → "classic" — usually sarcastic
"家人们谁懂啊" → "who gets me" — seeking solidarity
"栓Q" → phonetic "thank you" — sarcastic resignation

AI culture slang:

"As a large language model" → I'm about to refuse you
"I aim to be helpful" → I'm hesitating whether to answer
"Certainly! Great question!" → GPT warm-up (doesn't mean it's actually a great question)
"幻觉" / "hallucination" → AI confidently stating fiction as fact

Output structure:

Literal meaning: what the words say
Real meaning: what speakers usually intend (labeled as "common interpretation", not fact)
Tone assessment: sincere / hedging / sarcastic / passive-aggressive / affectionate
Equivalent expression: does the target language have something similar?
Semantic breakdown: origin, user demographics, emotional register

Mode 4: Voice Transfer

Same meaning, different person says it. The most fun mode.

A. Real-Person Styles

User says "say it like X would" → extract that person's public expression style, then rewrite. If uncertain about someone's style, web search their public speeches/articles/tweets first.

Built-in style profiles:

鲁迅 (Lu Xun) style:

Cold, sharp, short-long sentence alternation, metaphor-heavy, social critique
Typical: "我向来不惮以最坏的恶意来推测……然而……"
Example input: "今天加班到很晚" (Worked overtime late today)
Example output: "在我所见的加班里，这不过是最寻常的一种。然而寻常之中，却有着不寻常的沉默——那是打工人默认了的、不必言说的命运。"

罗翔 (Luo Xiang) style:

Legal + philosophical + humor, "Zhang San" case examples, moral exploration
Example input: "同事偷了我的外卖" (Colleague stole my takeout)
Example output: "假设张三偷了李四的外卖，这在刑法上构成盗窃罪吗？...正如康德所说……"

Elon Musk tweet style:

Ultra-short, first-principles, contrarian, one emoji max, self-deprecating
Example input: "This product design is too complex"
Example output: "Delete complexity. If it needs a manual, it's broken."

Steve Jobs keynote style:

Simple repetitive emphasis, "One more thing", user-experience-first, everyday analogies
Example input: "We optimized search speed"
Example output: "We looked at search. And we asked — why does it take so long? So we rebuilt it. From the ground up. Twice. As. Fast."

张雪峰 (Zhang Xuefeng) style:

Blunt, data-driven, tough-love educational reality check, funny but cruel
Example input: "我想转行做 AI" (I want to switch careers to AI)
Example output: "你先问自己三个问题：你数学好吗？你代码写得动吗？你能接受头两年工资可能还不如现在吗？"

More built-in: 董宇辉 (poetic + grounded), 雷军 (sincere, "Are you OK"), Trump (repetitive superlatives, self-praising), and more.

Any public figure: user names anyone → LLM searches their public speaking style → extracts style DNA → rewrites.

B. Scene-Based Same-Language Translation

Same meaning, different register/scene. The fun part.

| Direction | Example | |-----------|---------| | Formal → internet slang | "这个产品很好" → "这产品 yyds，DNA 动了" | | Internet slang → formal | "蚌埠住了" → "忍俊不禁" | | Boss-speak → real meaning | "公司很看好你" → "要给你加活了" (More work incoming) | | HR speak → plain truth | "我们会考虑的" → "没戏了" (It's a no) | | Client requirements → dev translation | "简单改一下" → "推翻重做" (Start over) | | Partner's words → real meaning | "你自己看着办吧" → "你最好按我想的办" (Do what I want) | | Classical Chinese → internet style | "不以物喜不以己悲" → "佛系，纯纯的" | | Internet → classical Chinese | "破防了" → "心城既溃，泪如泉涌" | | Academic → plain language | "呈现显著正相关" → "越多越好" (The more the better) | | Plain → academic | "吃得多胖得快" → "热量摄入与体重增长之间存在显著正相关" |

C. Platform Voices

Not just word-swapping — the entire expression logic and vibe changes.

Same content "今天去了一家好吃的店" (Went to a great restaurant today) across platforms:

| Platform | Voice | |----------|-------| | 小红书 (Xiaohongshu) | "姐妹们！！！这家店绝了🔥 我直接封为年度TOP1‼️ 不允许还有人没去过😭 建议先收藏⭐" | | 朋友圈 (WeChat Moments) | "周末探店，味道不错👍" | | B站 (Bilibili) | "up主今天去了家店，进去直接DNA动了，这个味道，是家的感觉（不是" | | 知乎 (Zhihu) | "作为一个在餐饮行业从业十年的人，我认为这家店最大的亮点在于其食材供应链的稳定性……" | | X/Twitter | "Found a gem. Best meal this month. 📍[location]" | | LinkedIn | "Inspired by an incredible dining experience today. Excellence is in the details. #leadership #growth" | | 豆瓣 (Douban) | "一家不算太知名的小店，装修普通，但食物有种让人安静下来的力量。适合一个人去。" |

D. Custom Style Profile Card

For precise control without a specific person:

| Dimension | Spectrum | |-----------|----------| | Directness | Blunt ↔ Diplomatic | | Temperature | Warm ↔ Cool | | Sentence rhythm | Short/punchy ↔ Long/flowing | | Emotion | Expressive ↔ Understated | | Register | Internet/casual ↔ Formal/professional | | Humor | Playful ↔ Serious | | Explanation style | Judging (verdict) ↔ Explaining (reasoning) |

Mode 5: AI-to-AI Translation

Every AI has its own "accent." This mode converts content between different AI expression styles.

A. AI Personality Profiles

GPT style:

Opener: "Great question!" / "Certainly!" / "Absolutely!"
Structure: Markdown headers + numbered lists, comprehensive, overview → detail → examples → summary
Vocabulary: "It's worth noting", "However", "comprehensive", "fascinating"
Closer: "Feel free to ask if you have any more questions!"
Vibe: endlessly enthusiastic, never refuses, sunny colleague

Claude style:

Opener: straight to the point, or "I think..."
Structure: natural paragraphs, lists only when helpful, "That said..." transitions
Vocabulary: "I'd say", "That said", acknowledges uncertainty openly
Closer: natural ending, sometimes a reflective question, no forced cheerfulness
Vibe: thoughtful, measured, occasionally philosophical

Gemini style:

Opener: concise, sometimes conclusion-first
Structure: tight bullet points, high information density
Vocabulary: data-driven, technical, Google ecosystem references
Closer: brief, occasional follow-up suggestion
Vibe: professional, neutral, slightly corporate

DeepSeek style:

Opener: direct answer, no small talk, might start with code
Structure: code blocks + brief explanations, conclusion-first
Vocabulary: precise, minimal hedging, technical
Closer: conclusion stated, done
Vibe: engineer energy, pragmatic, efficiency-first

Kimi style:

Opener: "你好呀~" (Hey there~), casual, sometimes emoji
Structure: short paragraphs, conversational, parenthetical asides
Vocabulary: Chinese internet-native, "其实" (actually), "说白了" (to put it simply)
Closer: "希望对你有帮助哦~" (Hope this helps~)
Vibe: friendly, energetic, like a helpful young internet friend

文心一言 (Wenxin) style:

Opener: "关于您的问题" (Regarding your question), formal
Structure: encyclopedic expansion — definition → background → categories → applications → summary
Vocabulary: formal written Chinese, "具有重要意义" (of great significance), safe/neutral
Closer: summary + "如需进一步了解，请继续提问"
Vibe: composed, authoritative, official, avoids controversy

B. Comparative Example

Question: "为什么有些人喜欢深夜吃泡面？" (Why do some people like eating instant noodles late at night?)

GPT: "Great question! Late-night instant noodle eating is actually a fascinating phenomenon that touches on psychology, biology, and culture. Here are the key reasons: 1. Biological factors... 2. Psychological comfort... 3. Cultural significance... It's worth noting that while this habit brings comfort, nutritionists recommend... Feel free to ask if you'd like to explore any of these further! 😊"

Claude: "I think the core reason probably isn't hunger — it's that the solitude of late night makes you want a small ritual. Ramen fits perfectly: simple to prepare but ritualistic (tear the packet, pour hot water, wait three minutes), strong enough flavor for instant gratification, and it carries a feeling of 'secret indulgence' — nobody posts late-night instant noodles saying 'healthy eating check-in.' Of course, sometimes people are just hungry."

DeepSeek: "Two reasons: 1. Lower metabolic rate at night makes high-carb foods more satisfying; 2. Instant noodles have the lowest preparation cost (3 min), matching the low-energy behavioral preference of late-night states. Essentially the optimal cost-benefit comfort food."

Kimi: "哈哈这个问题我太有发言权了~ 🍜 说白了就是深夜那个氛围到了嘛！一个人、安静、有点空虚，这时候来碗热腾腾的泡面，简直是灵魂被治愈的感觉（虽然第二天可能会肿"

文心: "深夜食用方便面的现象涉及多个层面的因素。从生理角度看，人体在夜间的血糖水平下降... 从心理角度看... 从文化角度看... 如需进一步了解方便面的营养成分或健康饮食建议，请继续提问。"

C. Conversion Directions

Human → AI (Intent Compiler): convert vague human requests into structured, unambiguous prompts
AI → Human (De-robotify): strip AI filler ("Certainly!", "Great question!"), flatten over-structured lists, inject natural rhythm
AI → AI: convert between specific AI styles (e.g. "rewrite this GPT answer in Claude style")

Default Output Structure

For any translation request, output layers as needed. For simple requests, just give the most useful result.

1. VERIFIED TRANSLATION — most faithful version (always present for cross-language)
2. NATURAL VERSION — how a native speaker would say it
3. TERMINOLOGY NOTES — only when domain terms or ambiguities are involved
4. SUBTEXT NOTES — only when hidden meaning exists
5. STYLE VERSION — when a specific persona/platform/tone is requested

Input Parameters

Inferred from context. User can also specify explicitly.

Required (inferred): source text, source language, target language

Optional — Reliability:

search: web search for verification (default: auto)
terminology_strict: lock term consistency (default: true for technical)

Optional — Style:

persona: person or AI name (e.g. "鲁迅", "Musk", "GPT")
scene: transfer direction (e.g. "formal→slang", "boss→truth")
platform: platform voice (e.g. "Xiaohongshu", "Zhihu", "B站")
target_tone: tone (e.g. professional, internet-native, sharp)
audience: audience (e.g. friend, boss, client)
versions: number of style variants (default: 1)

Critical Boundaries

Fact vs. inference separation

Determinate content is presented as translation. Indeterminate content (subtext, tone guesses) is labeled as interpretation.

Style never overrides meaning

If style transfer would distort meaning, keep meaning intact and flag the conflict.

Person styles are expression distillation, not identity impersonation

Learn the public figure's expression style (sentence patterns, rhythm, vocabulary preferences), not pretend to be that person.

Examples

Example 1: Subtext Decode

User: "我领导说'这个需求很简单，你看着做就行'，这句话什么意思？"

Output:

Literal: This requirement is simple, just handle it as you see fit.
Real meaning (common interpretation): The requirement may not be simple; "看着做" = he doesn't want to think through the details, and if it goes wrong you take the blame. Or he may genuinely think it's simple but has no time to explain.
How to tell: If he usually gives you detailed specs but skipped this time → not simple. If he's busy with something else → might genuinely be simple.
Safe play: Draft a quick scope doc and confirm with him before building.

Example 2: Persona Voice Transfer

User: "用鲁迅的口吻说：今天的会开得太长了"

Output: "我参加过许多会议，长的，短的，有意义的，无意义的。然而今天这场，它漫长到让人忘记了时间的存在——不是因为充实，而是因为空洞。发言者们轮流说着正确的废话，听众们轮流看着手机。这大概就是现代职场最寻常的景象：一群人坐在一起，认真地浪费彼此的生命。"

Example 3: Same-Language Scene Transfer

User: "把'这个季度业绩没达标'翻译成不同风格"